Nathan’s Homework 5

library(tidyverse)

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library(pastecs)

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library("readxl")

HousingData <- read_csv("Table8.csv")

## Rows: 3221 Columns: 272
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## chr   (6): source, sumlevel, geoid, name, st, cnty
## dbl (266): T8_est1, T8_est2, T8_est3, T8_est4, T8_est5, T8_est6, T8_est7, T8...
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cor(HousingData %>% select(T8_est69, T8_est3))

##           T8_est69   T8_est3
## T8_est69 1.0000000 0.8999879
## T8_est3  0.8999879 1.0000000

This is comparing owner occupied housing vs renter occupied that has less than or equal to 30% HAMFI

pairs(HousingData %>% select(T8_est69, T8_est3),
      main = "Scatterplot of CHAS Table 8 Variables")

cor.test(HousingData$T8_est69, HousingData$T8_est3, method = "kendall")

## 
##  Kendall's rank correlation tau
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## data:  HousingData$T8_est69 and HousingData$T8_est3
## z = 66.232, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.7804627

I chose Kendall’s Tau test because it makes fewer assumptions than the others. These results (high correlation with very low margin of error) means that there is a significant, positive correlation between the data for renter vs owner households, but it is not exactly the same either.